An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit.
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| Title: | An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit. |
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| Authors: | Mishra, Arabinda1 arabinda.mishra@vanderbilt.edu, Anderson, Adam W.1,2, Xi Wu1, Gore, John C.2, Zhaohua Ding1,2,3 |
| Source: | Medical Physics. Aug2010, Vol. 37 Issue 8, p4274-4287. 14p. 2 Color Photographs, 2 Charts, 4 Graphs. |
| Subjects: | Algorithms, Brain imaging, Periaqueductal gray matter, Medical imaging systems, Anisotropy |
| Abstract: | Purpose: The purpose of this work is to design a neuronal fiber tracking algorithm, which will be more suitable for reconstruction of fibers associated with functionally important regions in the human brain. The functional activations in the brain normally occur in the gray matter regions. Hence the fibers bordering these regions are weakly myelinated, resulting in poor performance of conventional tractography methods to trace the fiber links between them. A lower fractional anisotropy in this region makes it even difficult to track the fibers in the presence of noise. In this work, the authors focused on a stochastic approach to reconstruct these fiber pathways based on a Bayesian regularization framework. Methods: To estimate the true fiber direction (propagation vector), the a priori and conditional probability density functions are calculated in advance and are modeled as multivariate normal. The variance of the estimated tensor element vector is associated with the uncertainty due to noise and partial volume averaging (PVA). An adaptive and multiple sampling of the estimated tensor element vector, which is a function of the pre-estimated variance, overcomes the effect of noise and PVA in this work. Results: The algorithm has been rigorously tested using a variety of synthetic data sets. The quantitative comparison of the results to standard algorithms motivated the authors to implement it for in vivo DTI data analysis. The algorithm has been implemented to delineate fibers in two major language pathways (Broca’s to SMA and Broca’s to Wernicke’s) across 12 healthy subjects. Though the mean of standard deviation was marginally bigger than conventional (Euler’s) approach [P. J. Basser et al., “In vivo fiber tractography using DT-MRI data,” Magn. Reson. Med. 44(4), 625–632 (2000)], the number of extracted fibers in this approach was significantly higher. The authors also compared the performance of the proposed method to Lu’s method [Y. Lu et al., “Improved fiber tractography with Bayesian tensor regularization,” Neuroimage 31(3), 1061–1074 (2006)] and Friman’s stochastic approach [O. Friman et al., “A Bayesian approach for stochastic white matter tractography,” IEEE Trans. Med. Imaging 25(8), 965–978 (2006)]. Overall performance of the approach is found to be superior to above two methods, particularly when the signal-to-noise ratio was low. Conclusions: The authors observed that an adaptive sampling of the tensor element vectors, estimated as a function of the variance in a Bayesian framework, can effectively delineate neuronal fibers to analyze the structure-function relationship in human brain. The simulated and in vivo results are in good agreement with the theoretical aspects of the algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 52616600 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mishra%2C+Arabinda%22">Mishra, Arabinda</searchLink><relatesTo>1</relatesTo><i> arabinda.mishra@vanderbilt.edu</i><br /><searchLink fieldCode="AR" term="%22Anderson%2C+Adam+W%2E%22">Anderson, Adam W.</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Xi+Wu%22">Xi Wu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gore%2C+John+C%2E%22">Gore, John C.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhaohua+Ding%22">Zhaohua Ding</searchLink><relatesTo>1,2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Aug2010, Vol. 37 Issue 8, p4274-4287. 14p. 2 Color Photographs, 2 Charts, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+imaging%22">Brain imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Periaqueductal+gray+matter%22">Periaqueductal gray matter</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Anisotropy%22">Anisotropy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: The purpose of this work is to design a neuronal fiber tracking algorithm, which will be more suitable for reconstruction of fibers associated with functionally important regions in the human brain. The functional activations in the brain normally occur in the gray matter regions. Hence the fibers bordering these regions are weakly myelinated, resulting in poor performance of conventional tractography methods to trace the fiber links between them. A lower fractional anisotropy in this region makes it even difficult to track the fibers in the presence of noise. In this work, the authors focused on a stochastic approach to reconstruct these fiber pathways based on a Bayesian regularization framework. Methods: To estimate the true fiber direction (propagation vector), the a priori and conditional probability density functions are calculated in advance and are modeled as multivariate normal. The variance of the estimated tensor element vector is associated with the uncertainty due to noise and partial volume averaging (PVA). An adaptive and multiple sampling of the estimated tensor element vector, which is a function of the pre-estimated variance, overcomes the effect of noise and PVA in this work. Results: The algorithm has been rigorously tested using a variety of synthetic data sets. The quantitative comparison of the results to standard algorithms motivated the authors to implement it for in vivo DTI data analysis. The algorithm has been implemented to delineate fibers in two major language pathways (Broca’s to SMA and Broca’s to Wernicke’s) across 12 healthy subjects. Though the mean of standard deviation was marginally bigger than conventional (Euler’s) approach [P. J. Basser et al., “In vivo fiber tractography using DT-MRI data,” Magn. Reson. Med. 44(4), 625–632 (2000)], the number of extracted fibers in this approach was significantly higher. The authors also compared the performance of the proposed method to Lu’s method [Y. Lu et al., “Improved fiber tractography with Bayesian tensor regularization,” Neuroimage 31(3), 1061–1074 (2006)] and Friman’s stochastic approach [O. Friman et al., “A Bayesian approach for stochastic white matter tractography,” IEEE Trans. Med. Imaging 25(8), 965–978 (2006)]. Overall performance of the approach is found to be superior to above two methods, particularly when the signal-to-noise ratio was low. Conclusions: The authors observed that an adaptive sampling of the tensor element vectors, estimated as a function of the variance in a Bayesian framework, can effectively delineate neuronal fibers to analyze the structure-function relationship in human brain. The simulated and in vivo results are in good agreement with the theoretical aspects of the algorithm. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1118/1.3456113 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 4274 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Brain imaging Type: general – SubjectFull: Periaqueductal gray matter Type: general – SubjectFull: Medical imaging systems Type: general – SubjectFull: Anisotropy Type: general Titles: – TitleFull: An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mishra, Arabinda – PersonEntity: Name: NameFull: Anderson, Adam W. – PersonEntity: Name: NameFull: Xi Wu – PersonEntity: Name: NameFull: Gore, John C. – PersonEntity: Name: NameFull: Zhaohua Ding IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 37 – Type: issue Value: 8 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
